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Empirical analysis of ARMA-GARCH models in market risk estimation on high-frequency US data

Author

Listed:
  • Beck Alexander

    (Karlsruhe Institute of Technology, Karlsruher Str 88, 76139 Karlsruhe, Germany)

  • Kim Young Shin Aaron

    (KIT, Germany)

  • Rachev Svetlozar

    (University of Karlsruhe)

  • Feindt Michael

    (Karlsruhe Insitute of Technology and Phi-T)

  • Fabozzi Frank

    (EDHEC Business School, Lille, France)

Abstract

In this paper, we examine the S&P 500 index log-returns on short intraday time scales with three different ARMA-GARCH models. In order to forecast market risk, we describe the innovation process with tempered stable distributions which we compare to commonly used methods in financial modeling. Value-at-risk backtests are provided where we find that models based on the tempered stable innovation assumption significantly outperform traditional models in forecasting risk on short time-scales. In addition to value-at-risk, the idiosyncratic differences in average value-at-risk are compared between the models.

Suggested Citation

  • Beck Alexander & Kim Young Shin Aaron & Rachev Svetlozar & Feindt Michael & Fabozzi Frank, 2013. "Empirical analysis of ARMA-GARCH models in market risk estimation on high-frequency US data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 167-177, April.
    • Handle: RePEc:bpj:sndecm:v:17:y:2013:i:2:p:167-177:n:5
    DOI: 10.1515/snde-2012-0033
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    References listed on IDEAS

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    Cited by:

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    3. Choi, Jaehyung & Kim, Young Shin & Mitov, Ivan, 2015. "Reward-risk momentum strategies using classical tempered stable distribution," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 194-213.
    4. Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023. "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers 2023-20, Faculty of Economic Sciences, University of Warsaw.
    5. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
    6. Micah Goldblum & Avi Schwarzschild & Ankit B. Patel & Tom Goldstein, 2020. "Adversarial Attacks on Machine Learning Systems for High-Frequency Trading," Papers 2002.09565, arXiv.org, revised Oct 2021.

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